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Biomass Mapping using LIDAR and RADAR Data in Tropical and Boreal Forests

This overview explores the use of LIDAR and RADAR data to estimate forest biomass in tropical and boreal forest areas. It discusses the methodologies, data acquisition, and model-assisted estimation of biomass using these evolving technologies.

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Biomass Mapping using LIDAR and RADAR Data in Tropical and Boreal Forests

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  1. Module 2.8 Overview and status of evolving technologies Module developers: Brice Mora, Wageningen University Erika Romijn, Wageningen University Country examples: • Tropical biomass mapping in Kalimantan by integrating ALOS PALSAR and LIDAR data • Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area Source: US Forest Service. V1, May 2015 Creative Commons License

  2. 1. Tropical biomass mapping in Kalimantan by integrating ALOS PALSAR and LIDAR data • Study from Quinones et al. (2014) on estimating tropical forest biomass in Kalimantan using a combination of RADAR and LIDAR • Advantage of RADAR: works under cloudy conditions • Limitations of RADAR: saturation effects and speckle • Using RADAR in combination with LIDAR can help to overcome the limitations

  3. Classification of forest structural types using RADAR data • Image processing chain: • Data import and metadata extraction, radiometric calibration, coarse geocoding, fine geocoding, and geometric and radiometric terrain correction • Preprocessing: • Strip selection, radiometric correction, ortho-rectification, slope correction, and mask preparation • Classification  17 strata • Unsupervised segmentation, postprocessing, validation, and LCCS labelling

  4. Vegetation structural type, Kalimantan

  5. Generation of vegetation height map through fusion of LIDAR and RADAR data LIDAR height histograms for each stratum • Extraction of vegetation height from LIDAR data for 100,000 points: histogram with distribution of heights for each vegetation structure type (stratum) • Matching of LIDAR height histograms with ALOS PALSAR HV histograms for each vegetation structure type  height map for whole Kalimantan RADAR HV histograms

  6. Tropical biomass mapping • Use of 3 different equations to calculate biomass based on the height map: - Bio1 = Height^1.68 - Bio2 = 0.06328*(Height^2.4814) - Bio3 = 9.875+0.04552*(Height^2.5734) • Map validation with biomass estimates from field data • Use of 3 different equations to calculate biomass based on field data: - Ketterings et al. (2001) BIO = 0.066*D^2.59 - Kenzo et al. (2009) BIO = 0.0829*D^2.43 - Brown (1997) BIO = 0.118*D^2.53

  7. Tropical biomass mapping • Use of 3 different equations to calculate biomass based on the height map: - Bio1 = Height^1.68 - Bio2 = 0.06328*(Height^2.4814) - Bio3 = 9.875+0.04552*(Height^2.5734) • Map validation with biomass estimates from field data • Use of 3 different equations to calculate biomass based on field data: - Ketterings et al. (2001) BIO = 0.066*D^2.59 - Kenzo et al. (2009) BIO = 0.0829*D^2.43 - Brown (1997) BIO = 0.118*D^2.53

  8. 2. Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area Naesset et al. (2011),“Model-assisted Regional Forest Biomass Estimation Using LIDAR and InSAR as Auxiliary Data: A Case Study From a Boreal Forest Area” • Enhancing biomass estimation with input from forest structure parameters, which were measured with LIDAR and InSAR techniques

  9. Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area Methodology • Stratification of forest land into four strata, through interpretation of aerial photographs (photogrammetry) • Collecting field data: • For sample survey plots and large field plots • For measurements of tree diameter (dbh) and tree height • Computed from field measurements: Lorey’s mean height hL, basal area (G), number of trees per hectare (N) • Acquiring LIDAR and InSAR data

  10. Acquiring LIDAR and InSAR data • Acquiring LIDAR data for each grid cell of the study area: • Canopy height distributions, including order statistics: height deciles and maximum height value • Canopy density distributions • Acquiring SRTM InSAR (X-band) data to produce a digital surface model (DSM) and digital height error model (HEM) and two datasets of pixel-level canopy heights: • Subtracting the LIDAR terrain model from InSAR DSM • Subtracting the terrain model generated from official topographic map from the InSAR DSM

  11. Estimation of aboveground biomass • Estimation of aboveground biomass (AGB) from field data: • Using dbh and tree height as independent variables to estimate the mean biomass per hectare for each stratum, which is called “observed biomass” • Model-assisted and model-based regression to estimate AGB, using LIDAR and InSAR as auxiliary data: • Using variables from canopy height distributions obtained with LIDAR for 4 forest strata • Using the 2 InSAR height variables for 4 forest strata • Difference between observed biomass and model-assisted estimation of biomass using LIDAR and InSAR data was calculated

  12. Comparison of model-assisted estimation of biomass and observed biomass Observed biomass (Mg/ha) Observed biomass (Mg/ha) Observed biomass (Mg/ha) Predicted biomass (Mg/ha) Predicted biomass (Mg/ha) Predicted biomass (Mg/ha) LIDAR estimates InSARTopo estimates InSARLIDAR estimates Source: Naesset et al. 2011, fig. 2.

  13. Conclusions: Use for tropical biomass estimation • LIDAR: • Promising for tropical biomass estimation • High accuracy and high precision of estimates • However, monitoring costs are high • InSAR: • Moderate accuracy and precision • RADAR: ability to see through clouds • Frequent updates at low costs • Useful when accurate terrain model is used—however, these are not widely available in the tropics

  14. Recommended modules as follow-up • Modules 3.1 to 3.3 to proceed with REDD+ assessment and reporting

  15. References • Brown, S., 1997. Estimating Biomass and Biomass Change of Tropical Forests: a Primer (FAO Forestry Paper-134), FAO, United Nations, Rome. • Di Gregorio, A., and Louisa J.M. Jansen. 2000. Land Cover Classification System (LCCS): Classification Concepts and User Manual. Rome: Food and Agricultural Organization. http://www.fao.org/docrep/003/x0596e/X0596e00.htm#P-1_0. • IPCC (Intergovernmental Panel on Climate Change). 2000. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. (Often IPCC GPG.) Geneva, Switzerland: IPCC. http://www.ipcc-nggip.iges.or.jp/public/gp/english/. • Kenzo, T., R. Furutani, D. Hattori, J. J. Kendawang, S. Tanaka, K. Sakurai, and I. Ninomiya. 2009. “Allometric Equations for Accurate Estimation of Aboveground Biomass in Logged-over Tropical Rainforests in Sarawak, Malaysia.” Journal of Forest Research 14 (6): 365–372. doi:10.1007/s10310-009-0149-1 • KetteringsQ. M., R. Coe, M. van Noordwijk. 2001. “Reducing Uncertainty in the Use of Allometric Biomass Equations for Predicting Aboveground Tree Biomass in Mixed Secondary Forests.” Forest Ecology and Management 146: 199–209.

  16. Næsset, E., Gobakken, T., Solberg, S., Gregoire, T.G., Nelson, R., Ståhl, G., Weydahl, D., 2011. “Model-assisted Regional Forest Biomass Estimation Using LiDAR and InSAR as Auxiliary Data: A Case Study from a Boreal Forest Area.” Remote Sensing of Environment 115 (12): 3599-3614. • Quinones, M., C. Van der Laan, D. Hoekman, and V. Schut., 2014. Integration of AlosPalSAR and LIDAR IceSAT data in a multistep approach for wide area biomass mapping. Presentation Living Planet, Edinburg, September 2013.

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